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MidSemI: A Middleware for Semantic Integration of Business Data with Large-scale Social and Linked Data

Author

Listed:
  • Samir Sellami

    (LIRE Laboratory, University of Constantine 2 - Abdelhamid Mehri, Constantine, Algeria)

  • Taoufiq Dkaki

    (IRIT Laboratory, University of Toulouse 2 - Jean Jaurès, Toulouse, France)

  • Nacer Eddine Zarour

    (LIRE Laboratory, University of Constantine 2 - Abdelhamid Mehri, Constantine, Algeria)

  • Pierre-Jean Charrel

    (IRIT Laboratory, University of Toulouse 2 - Jean Jaurès, Toulouse, France)

Abstract

The web diversification into the Web of Data and social media means that companies need to gather all the necessary data to help make the best-informed market decisions. However, data providers on the web publish data in various data models and may equip it with different search capabilities, thus requiring data integration techniques to access them. This work explores the current challenges in this area, discusses the limitations of some existing integration tools, and addresses them by proposing a semantic mediator-based approach to virtually integrate enterprise data with large-scale social and linked data. The implementation of the proposed approach is a configurable middleware application and a user-friendly keyword search interface that retrieves its input from internal enterprise data combined with various SPARQL endpoints and Web APIs. An evaluation study was conducted to compare its features with recent integration approaches. The results illustrate the added value and usability of the contributed approach.

Suggested Citation

  • Samir Sellami & Taoufiq Dkaki & Nacer Eddine Zarour & Pierre-Jean Charrel, 2019. "MidSemI: A Middleware for Semantic Integration of Business Data with Large-scale Social and Linked Data," International Journal of Information System Modeling and Design (IJISMD), IGI Global, vol. 10(2), pages 1-25, April.
    • Handle: RePEc:igg:jismd0:v:10:y:2019:i:2:p:1-25
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